Extrinsic parameters of a camera include, in particular, a position and an alignment or respectively an angle of the camera relative to a vehicle, on which the camera is mounted, wherein the angles or respectively positions also have to be additionally determined relative to the roadway for lane algorithms. The establishment of the extrinsic parameters therefore involves, in particular, an estimation of an orientation of the camera affixed to the vehicle relative to a coordinate system of the vehicle. The establishment of the camera orientation with regard to the vehicle typically provides a snapshot, typically within the context of a so-called “end-of-line” calibration at the end of a production cycle of the vehicle, wherein e.g. calibration patterns or laser techniques are used.
Such calibration procedures, which are typically carried out in a production facility in which the vehicle is manufactured or at least assembled and tested prior to being delivered to a customer, frequently cause relatively high costs. Furthermore, an estimation of the relative installation position and of the relative angle of the camera with respect to the vehicle can contain inaccuracies. Moreover, the calibration merely provides a snapshot. However, the extrinsic camera parameters change over the time that the vehicle is used. The reason for this is, in particular, external influences. Thus, e.g. braking operations and acceleration operations lead to a change in the pitch angle relative to the road and a(n) (uneven) loading of the vehicle can result in a change in the height of the camera and can change the angle of roll of the vehicle. In addition, temperature influences and loads over time can lead to a decalibration of the camera.
Furthermore, so-called “online” calibration methods are known, which are based on an extraction of objects/features (for example, lane markers or ground textures), as a result of which the possible area of application is, however, restricted. Geometric assumptions can also be made (for example, the so-called “flat world” assumption or a horizon estimation), or sensor data can be used (for example, by radar object recognition), in order to determine or respectively estimate the extrinsic parameters of the camera.
In their article “Selfcalibration system for the orientation of a vehicle camera” (published in Proc. of the ISPRS Com. V Symposium: Image Engineering and Vision Metrology, 2006, pages 68-73), Á. Catalá-Prat, J. Rataj and R. Reulke describe a calibration method which is based on a detection of road markings. Similar concepts are described by S. Hold, S. Gormer, A. Kummert, M. Meuter and S. Muller-Schneiders in their article “A novel approach for the online initial calibration of extrinsic parameters for a carmounted camera” (published in ITSC'09. 12th International IEEE Conference on Intelligent Transportation Systems, 2009, IEEE, 2009, pages 1-6), or by M. Wu and X. An in their article “An automatic extrinsic parameter calibration method for camera-on-vehicle on structured road” (published in IC-VES. IEEE International Conference on Vehicular Electronics and Safety, 2007, IEEE, 2007, pages 1-5).
In addition, in their article “Homography-based extrinsic selfcalibration for cameras in automotive applications” (published in Workshop on Intelligent Transportation, 2010, pages 17-22), M. Miksch, B. Yang, and K Zimmermann present a system which uses a natural texture of a roadway for the calibration, wherein a flat roadway is assumed, the location of which in the image has to be known, as a result of which the system is dependent on the vehicle odometry.
Camera data of the vehicle are typically used following a coordinate transformation into the vehicle coordinates and following a subsequent transformation into the roadway coordinates. In this case, the positional accuracy of the camera data processing is directly proportional to the accuracy of the camera parameter estimation. A level of accuracy requiring a high quality in the estimation or respectively establishment of the extrinsic parameters of the camera of the vehicle is thus needed for a transformation into another coordinate system, e.g. for a fusion with other sensor data in the driver assistance field.
As such, it is desirable to present an alternative method for automatically establishing or respectively estimating extrinsic parameters of a camera of a vehicle, wherein the method solves the problems of the prior art, which are described above. In addition, other desirable features and characteristics will become apparent from the subsequent summary and detailed description, and the appended claims, taken in conjunction with the accompanying drawings and this background.